The Influence of Ridership Weighting on Targeting and Recovery Strategies for Urban Rail Rapid Transit Systems

TL;DR

This study investigates how incorporating low-resolution ridership data into network models affects the effectiveness of attack and recovery strategies in urban rail systems, highlighting the importance of tailored resilience planning.

Contribution

It systematically analyzes the impact of using coarsely-averaged ridership data on topological network resilience strategies across multiple transit systems.

Findings

Centrality-based strategies excel with GCC performance measure.

Hybrid strategies combining centrality and ridership perform best with HRCC.

Effectiveness of strategies varies by network characteristics and emergency goals.

Abstract

The resilience of urban rapid transit systems (URTs) to a rapidly evolving threat space is of much concern. Extreme rainfall events are both intensifying and growing more frequent under continuing climate change, exposing transit systems to flooding, while cyber threats and emerging technologies such as unmanned aerial vehicles are exposing such systems to targeted disruptions. An imperative has emerged to model how networked infrastructure systems fail and devise strategies to efficiently recover from disruptions. Passenger flow approaches can quantify more dimensions of resilience than network science-based approaches, but the former typically requires granular data from automatic fare collection and suffers from large runtime complexities. Some attempts have been made to include accessible low-resolution ridership data in topological frameworks. However, there is yet to be a systematic investigation of the effects of incorporating low-dimensional, coarsely-averaged ridership volume into topological network science methodologies. We simulate targeted attack and recovery sequences using station-level ridership, four centrality measures, and weighted combinations thereof. Resilience is quantified using two topological measures of performance: the node count of a network's giant connected component (GCC), and a new measure termed the "highest ridership connected component" (HRCC). Three transit systems are used as case studies: the subways of Boston, New York, and Osaka. Results show that centrality-based strategies are most effective when measuring performance via GCC, while centrality-ridership hybrid strategies perform strongest by HRCC. We show that the most effective strategies vary by network characteristics and the mission goals of emergency managers, highlighting the need to plan for strategic adversaries and rapid recovery according to each city's unique needs.